Abstract
Video surveillance is one of the primary and practical actions to prevent criminal and terrorist attacks. Nowadays all public areas are under video surveillance. Most of the surveillance cameras are installed in open spaces. However, the video data captured by the surveillance camera can be affected by the weather condition. In this paper, we are concentrating on the video data captured by surveillance cameras in rainy situation. We have proposed a deep learning-based method to detect object in rainy situations from videos. However, object detection in deep learning is the ubiquitous research topic in computer vision and analysis. Much work has been done in that area. However, deep learning-based object detection in rainy condition remains untouched. We have applied our proposed method for both daytime and nighttime. The method produces excellent results in all conditions.
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The authors are thankful to the RUSA 2.0 at Jadavpur University for supporting this work.
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Dey, R., Bhattacharjee, D., Nasipuri, M. (2020). Object Detection in Rainy Condition from Video Using YOLO Based Deep Learning Model. In: Chaki, R., Cortesi, A., Saeed, K., Chaki, N. (eds) Advanced Computing and Systems for Security. Advances in Intelligent Systems and Computing, vol 1136. Springer, Singapore. https://doi.org/10.1007/978-981-15-2930-6_10
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DOI: https://doi.org/10.1007/978-981-15-2930-6_10
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